Data Mining - Concepts and Techniques.

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.

Ontology-driven visualization system for semantic searching

Extracting local ontology from domain-specific documents for the purpose of acquiring knowledge or semantic information to extend their ontologies is considered very important Main components of ontology are concepts and relations between concepts In this paper, we focus on extracting triples, in which verbs are relations and subjects/objects are concepts, from documents based on natural language Further, we show that term frequency is the most reliable measure among tf-idf and entropy on evaluating relations extracted from documents, particularly the aircraft maintenance manual

Extracting Relations towards Ontology Extension

In this paper, we present a mobile collaborative outdoor augmented reality system. The system will provide a user who captures image of a building facade with related introduction. The proposed system mainly consists of two major parts - annotation module and AR browsing module. Annotation module works with existing standards and linked data to allow users create annotations of the building. Annotation data in our system which only contains URI and metadata, such cluster ID, cluster centroid and the image feature vectors. When user using the system browsing the landmark in outdoor, the system first recognize the landmark and calculate the camera pose from the matching, and then the related introduction is retrieved from linked data through its URI. Experiments show that the proposed augmented reality system is efficient and robust in many cases.

Mobile landmark guide system based on LOD and SURF

In semantic social network, the relations between users are inferred by measuring the similarity between the corresponding personal ontologies. However, "over-enriched" personal ontologies have caused some difficulties in being discriminated from other personal ontologies. For efficiently annotating resources in their own repositories, people simply append ontology fragments retrieved from standard ontologies and from other neighbors' personal ontologies along to social links. In this paper, we propose a preprocessing method to extract preferential concepts for comparing with social semantics. In order to prune out irrelevant concepts from personal ontologies, alignment-based concept classification process is designed by checking these two main criteria; i) redundancy (e.g., if there already exist semantically identical concepts), and ii) tendency (e.g., if there exist semantically declined concepts). Finally, we want to show an application scenario to demonstrate our contributions.

Alignment-Based Preprocessing of Personal Ontologies on Semantic Social Network

Previously common users just want to watch the video contents without any specific requirements or purposes. However, in today's life while watching video user attempts to know and discover more about things that appear on the video. Therefore, the requirements for finding multimedia or browsing information of objects that users want, are spreading with the increasing use of multimedia such as videos which are not only available on the internet-capable devices such as computers but also on smart TV and smart phone. In order to meet the users. requirements, labor-intensive annotation of objects in video contents is inevitable. For this reason, many researchers have actively studied about methods of annotating the object that appear on the video. In keyword-based annotation related information of the object that appeared on the video content is immediately added and annotation data including all related information about the object must be individually managed. Users will have to directly input all related information to the object. Consequently, when a user browses for information that related to the object, user can only find and get limited resources that solely exists in annotated data. Also, in order to place annotation for objects user's huge workload is required. To cope with reducing user's workload and to minimize the work involved in annotation, in existing object-based annotation automatic annotation is being attempted using computer vision techniques like object detection, recognition and tracking. By using such computer vision techniques a wide variety of objects that appears on the video content must be all detected and recognized. But until now it is still a problem facing some difficulties which have to deal with automated annotation. To overcome these difficulties, we propose a system which consists of two modules. The first module is the annotation module that enables many annotators to collaboratively annotate the objects in the video content in order to access the semantic data using Linked Data. Annotation data managed by annotation server is represented using ontology so that the information can easily be shared and extended. Since annotation data does not include all the relevant information of the object, existing objects in Linked Data and objects that appear in the video content simply connect with each other to get all the related information of the object. In other words, annotation data which contains only URI and metadata like position, time and size are stored on the annotation sever. So when user needs other related information about the object, all of that information is retrieved from Linked Data through its relevant URI. The second module enables viewers to browse interesting information about the object using annotation data which is collaboratively generated by many users while watching video. With this system, through simple user interaction the query is automatically generated and all the related information is retrieved from Linked Data and finally all the additional information of the object is offered to the user. With this study, in the future of Semantic Web environment our proposed system is expected to establish a better video content service environment by offering users relevant information about the objects that appear on the screen of any internet-capable devices such as PC, smart TV or smart phone.

Geun-Sik Jo

Papers

Ontology-driven visualization system for semantic searching

Extracting Relations towards Ontology Extension

Mobile landmark guide system based on LOD and SURF

Alignment-Based Preprocessing of Personal Ontologies on Semantic Social Network

A Collaborative Video Annotation and Browsing System using Linked Data